Introduction to Generalized
Linear Models
2007 CAS Predictive Modeling Seminar
Prepared by
Louise Francis
Francis Analytics and Actuarial Data Mining, Inc.
www.data-mines.com
Louise_francis@msn.com
October 11, 2007
Objectives
 Gentle introduction to Linear
Models
 Illustrate some simple applications
of linear models
 Address some practical modeling
issues
 Show features common to LMs
and GLMs
Predictive Modeling Family
Predictive Modeling
Classical Linear Models
GLMs
Data Mining
Many Aspects of Linear Models are Intuitive
Severity
An Introduction to Linear Regression
Year
Intro to Regression Cont.
 Fits line that minimizes squared deviation between actual and
fitted values

min
  (Y  Y ) 
2
i
Some Work Related Liability Data
Closed Claims from Tx Dept of Insurance
 Total Award
 Initial Indemnity reserve
 Policy Limit
 Attorney Involvement
 Lags
Closing
 Report
 Injury
 Sprain, back injury, death, etc

 Data, along with some of analysis will be posted on internet
Simple Illustration
Total Settlement vs. Initial Indemnity Reserve
How Strong Is Linear Relationship?:
Correlation Coefficient
 Varies between -1 and 1
 Zero = no linear correlation
lnInitialIndemnityRes
lnTotalAward
lnInitialExpense
lnReportlag
lnInitialIndemnityRes lnTotalAward lnInitialExpense lnReportlag
1.000
0.303
1.000
0.118
0.227
1.000
-0.112
0.048
0.090
1.000
Scatterplot Matrix
Prepared with Excel add-in XLMiner
Excel Does Regression
 Install Data
Analysis Tool
Pak (Add In) that
comes wit Excel
 Click Tools, Data
Analysis,
Regression
How Good is the fit?
First Step: Compute residual
 Residual = actual – fitted
Y=lnTotal
Award
10.13
14.08
10.31
Predicted Residual
11.76
-1.63
12.47
1.61
11.65
-1.34
 Sum the square of the residuals (SSE)
 Compute total variance of data with no
model (SST)
Goodness of Fit Statistics
 R2: (SSE Regression/SS Total)

percentage of variance explained
 Adjusted R2

R2 adjusted for number of coefficients in
model
 Note SSE = Sum squared errors
 MS is Mean Square Error
2
R
Statistic
Significance of Regression
 F statistic:
(Mean square error of Regression/Mean
Square Error of Residual)
 Df of numerator = k = number of predictor vars
 Df denominator = N - k

ANOVA (Analysis of Variance) Table
Goodness of Fit Statistics
 T statistics: Uses SE of coefficient to
determine if it is significant
SE of coefficient is a function of s (standard
error of regression)
 Uses T-distribution for test
 It is customary to drop variable if coefficient
not significant

T-Statistic: Are the Intercept and
Coefficient Significant?
Intercept
lnInitialIndemnity
Res
Coefficients
10.343
Standard
Error
0.112
0.154
0.011
t Stat
P-value
92.122
0
13.530
8.21E-40
Other Diagnostics: Residual Plot
Independent Variable vs. Residual
 Points should scatter randomly around zero
 If not, regression assumptions are violated
Predicted vs. Residual
Random Residual
DATA WITH NORMALLY DISTRIBUTED ERRORS RANDOMLY
GENERATED
What May Residuals Indicate?
 If absolute size of residuals increases as
predicted increases, may indicate nonconstant variance
may indicate need to log dependent variable
 May need to use weighted regression

 May indicate a nonlinear relationship
Standardized Residual: Find Outliers
N
zi 
( yi  yˆi )
 se
,  se =
2
ˆ
 (yi  yi )
i=1
N  k 1
Standardized Residuals by Observation
6
4
2
0
-2
0
500
1000
-4
Observation
1500
2000
Outliers
 May represent error
 May be legitimate but have undue influence
on regression
 Can downweight oultliers
Weight inversely proportional to variance of
observation
 Robust Regression



Based on absolute deviations
Based on lower weights for more extreme
observations
Non-Linear Relationship
Non-Linear Relationships
 Suppose Relationship between dependent and
independent variable is non-linear?
 Linear regression requires a linear relationship
Transformation of Variables
 Apply a transformation to either the
dependent variable, the independent variable
or both
 Examples:
Y’ = log(Y)
 X’ = log(X)
 X’ = 1/X
 Y’=Y1/2

Transformation of Variables: Skewness
of Distribution
Use Exploratory Data Analysis to detect skewness, and heavy tails
After Log Transformation
-Data much less skewed, more like Normal, though still skewed
Box Plot
Histogram
500
20
450
400
18
Y Values
14
12
10
8
6
4
2
0
Frequency
16
350
300
250
200
150
100
50
0
lnTotalAward
9.6
10.4 11.2
12
12.8 13.6 14.4 15.2
lnTotalAward
16
16.8 17.6
Transformation of Variables
 Suppose the Claim Severity is a function of the
log of report lag


Compute X’ = log(Report Lag)
Regress Severity on X’
Categorical Independent Variables:
The Other Linear Model: ANOVA
Average of Totalsettlementamountorcourtaward
Injury
Amputation
Backinjury
Braindamage
Burnschemical
Burnsheat
Circulatorycondition
Table above created with Excel Pivot Tables
Total
567,889
168,747
863,485
1,097,402
801,748
302,500
Model
 Model is Model Y = ai, where i is a category of
the independent variable. ai is the mean of
category i.
Drop Page Fields Here
A ver age Sever i t y B y I nj ur y
A ver age of T r ended Sever i t y
16, 000. 00
14, 000. 00
12, 000. 00
10, 000. 00
Drop Series Fields Here
8, 000. 00
6, 000. 00
4, 000. 00
2, 000. 00
B RUI SE
T ot al
4, 215. 78
B URN
2, 185. 64
CRUSHI
CUT / P U
NG
NCT
2, 608. 14
1, 248. 90
Y=a1
EYE
FRA CT U
OT HE R
SP RA I N
ST RA I N
6, 849. 98
3, 960. 45
7, 493. 70
RE
534. 23
14, 197. 4
Y=a
9
I nj ur y
Two Categories
 Model Y = ai, where i is a category of the
independent variable
 In traditional statistics we compare a1 to a2
If Only Two Categories: T-Test for test of Significance of
Independent Variable
Variable 1
Mean
124,002
Variance
2.35142E+11
Observations
354
Hypothesized Mean Difference
0
df
1591
t Stat
-7.17
P(T<=t) one-tail
0.00
t Critical one-tail
1.65
P(T<=t) two-tail
0.00
t Critical two-tail
1.96
Variable 2
440,758
1.86746E+12
1448
Use T-Test from Excel Data Analysis Toolpak
More Than Two Categories
 Use F-Test instead of T-Test
 With More than 2 categories, we refer to it as
an Analysis of Variance (ANOVA)
Fitting ANOVA With Two Categories
Using A Regression
 Create A Dummy Variable for Attorney
Involvement
 Variable is 1 If Attorney Involved, and 0
Otherwise
Attorneyinvolvement-insurer
Y
Y
Y
N
Y
N
Y
Y
N
Attorney TotalSettlement
1
25000
1
1300000
1
30000
0
42500
1
25000
0
30000
1
36963
1
145000
0
875000
More Than 2 Categories
 If there are K Categories Create k-1 Dummy Variables

Dummyi = 1 if claim is in category i, and is 0
otherwise
 The kth Variable is 0 for all the Dummies
 Its value is the intercept of the regression
Design Matrix
Injury Code
1
1
12
11
17
Injury_Backin Injury_Multipl Injury_Nervou
jury
einjuries
scondition
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
Injury_Other
Top table Dummy variables were hand coded, Bottom
table dummy variables created by XLMiner.
0
0
0
0
1
Regression Output for Categorical
Independent
A More Complex Model Multiple
Regression
• Let Y = a + b1*X1 + b2*X2 +
…bn*Xn+e
• The X’s can be numeric variables
or categorical dummies
Multiple Regression
Y = a + b1* Initial Reserve+ b2* Report Lag + b3*PolLimit
+ b4*age+ ciAttorneyi+dkInjury k+e
SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
0.49844
0.24844
0.24213
1.10306
1802
ANOVA
df
Regression
Residual
Total
Intercept
lnInitialIndemnityRes
lnReportlag
Policy Limit
Clmt Age
Attorney
Injury_Backinjury
Injury_Braindamage
Injury_Burnschemical
Injury_Burnsheat
Injury_Circulatorycondition
15
1786
1801
SS
718.36
2173.09
2891.45
Coefficients Standard Error
10.052
0.156
0.105
0.011
0.020
0.011
0.000
0.000
-0.002
0.002
0.718
0.068
-0.150
0.075
0.834
0.224
0.587
0.247
0.637
0.175
0.935
0.782
MS
47.89
1.22
t Stat
64.374
9.588
1.887
4.405
-1.037
10.599
-1.995
3.719
2.375
3.645
1.196
F
39.360
P-value
0.000
0.000
0.059
0.000
0.300
0.000
0.046
0.000
0.018
0.000
0.232
More Than One Categorical Variable
 For each categorical variable
 Create k-1 Dummy variables
 K is the total number of variables
 The category left out becomes the “base”
category
 It’s value is contained in the intercept
 Model is Y = ai + bj + …+ e or

Y = u+ai + bj + …+ e, where ai + bj
are offsets to u

e is random error term
Correlation of Predictor Variables:
Multicollinearity
Multicollinearity
Predictor variables are assumed
uncorrelated
• Assess with correlation matrix
•
Remedies for Multicollinearity
• Drop one or more of the highly correlated
variables
• Use Factor analysis or Principle components
to produce a new variable which is a
weighted average of the correlated variables
• Use stepwise regression to select variables
to include
Similarities with GLMs
Linear Models
 Transformation of
Variables
 Use dummy coding for
categorical variables
 Residual
 Test significance of
coefficients
GLMs
 Link functions
 Use dummy coding for
categorical variables
 Deviance
 Test significance of
coefficients
Introductory Modeling Library
Recommendations
• Berry, W., Understanding Regression Assumptions,
Sage University Press
• Iversen, R. and Norpoth, H., Analysis of Variance,
Sage University Press
• Fox, J., Regression Diagnostics, Sage University
Press
• Data Mining for Business Intelligence, Concepts,
Applications and Techniques in Microsoft Office
Excel with XLMiner,Shmueli, Patel and Bruce, Wiley
2007